Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	37,568
2	North Dakota	36,403
3	Mississippi	35,357
4	Florida	34,268
5	Alabama	33,871
6	South Dakota	32,696
7	Iowa	31,837
8	Tennessee	31,372
9	Arizona	31,059
10	Arkansas	30,978
11	South Carolina	30,697
12	Georgia	29,845
13	Texas	28,845
14	Nevada	28,061
15	Wisconsin	27,592
16	Nebraska	27,315
17	Idaho	27,230
18	Utah	27,099
19	Illinois	25,707
20	Oklahoma	25,318
21	Rhode Island	24,820
22	New York	24,676
23	Missouri	24,352
24	New Jersey	24,305
25	Kansas	23,716
26	Delaware	22,889
27	District of Columbia	22,702
28	North Carolina	22,221
29	Maryland	21,892
30	California	21,764
31	Indiana	20,637
32	Massachusetts	20,263
33	Minnesota	20,124
34	Virginia	18,694
35	Kentucky	18,594
36	Montana	17,969
37	Connecticut	17,215
38	Puerto Rico	16,981
39	New Mexico	15,910
40	Michigan	15,148
41	Alaska	14,770
42	Ohio	14,558
43	Pennsylvania	13,891
44	Colorado	13,804
45	Wyoming	13,480
46	Washington	12,904
47	West Virginia	10,200
48	Hawaii	9,658
49	Oregon	8,884
50	New Hampshire	6,772
51	Maine	4,279
52	Vermont	3,006

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	North Dakota	742
2	South Dakota	644
3	Montana	538
4	Nebraska	460
5	Wisconsin	447
6	Utah	371
7	Alaska	321
8	Tennessee	313
9	Wyoming	268
10	Oklahoma	260
11	Iowa	256
12	Indiana	250
13	Minnesota	243
14	Arkansas	240
15	Missouri	240
16	Kansas	237
17	Illinois	217
18	Idaho	184
19	Kentucky	182
20	Nevada	178
21	New Mexico	178
22	Alabama	177
23	Mississippi	173
24	North Carolina	168
25	Delaware	158
26	South Carolina	154
27	Colorado	140
28	Puerto Rico	140
29	Connecticut	125
30	Texas	120
31	Ohio	116
32	Virginia	114
33	Michigan	113
34	Georgia	111
35	Florida	110
36	West Virginia	106
37	Maryland	94
38	Massachusetts	94
39	Arizona	90
40	Pennsylvania	90
41	Louisiana	88
42	District of Columbia	84
43	New Jersey	78
44	California	73
45	Oregon	73
46	New York	62
47	Washington	62
48	New Hampshire	58
49	Hawaii	50
50	Maine	21
51	Vermont	16
52	Rhode Island	0

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,821
2	New York	1,691
3	Massachusetts	1,395
4	Connecticut	1,271
5	Louisiana	1,219
6	Rhode Island	1,066
7	Mississippi	1,041
8	District of Columbia	902
9	Arizona	791
10	Illinois	730
11	Michigan	723
12	Florida	717
13	South Carolina	691
14	Georgia	683
15	Delaware	673
16	Maryland	662
17	Pennsylvania	659
18	Texas	588
19	Indiana	563
20	Alabama	543
21	Nevada	542
22	Arkansas	525
23	Iowa	469
24	North Dakota	459
25	New Mexico	436
26	Ohio	428
27	California	419
28	Missouri	404
29	Tennessee	401
30	Virginia	393
31	Minnesota	389
32	Colorado	372
33	North Carolina	362
34	New Hampshire	335
35	South Dakota	325
36	Washington	300
37	Kentucky	290
38	Idaho	285
39	Oklahoma	279
40	Nebraska	273
41	Kansas	264
42	Wisconsin	255
43	Puerto Rico	230
44	West Virginia	214
45	Montana	198
46	Utah	163
47	Oregon	142
48	Hawaii	118
49	Maine	106
50	Wyoming	93
51	Vermont	92
52	Alaska	76

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	North Dakota	10
2	Arkansas	6
3	South Dakota	4
4	Florida	3
5	Georgia	2
6	Iowa	2
7	Louisiana	2
8	Mississippi	2
9	New Mexico	2
10	Tennessee	2
11	Wisconsin	2
12	Colorado	1
13	Delaware	1
14	District of Columbia	1
15	Illinois	1
16	Indiana	1
17	Kansas	1
18	Kentucky	1
19	Massachusetts	1
20	Minnesota	1
21	Missouri	1
22	Montana	1
23	Nebraska	1
24	Nevada	1
25	New Hampshire	1
26	Oklahoma	1
27	Pennsylvania	1
28	Puerto Rico	1
29	South Carolina	1
30	Texas	1
31	Utah	1
32	West Virginia	1
33	Alabama	0
34	Alaska	0
35	Arizona	0
36	California	0
37	Connecticut	0
38	Hawaii	0
39	Idaho	0
40	Maine	0
41	Maryland	0
42	Michigan	0
43	New Jersey	0
44	New York	0
45	North Carolina	0
46	Ohio	0
47	Oregon	0
48	Rhode Island	0
49	Vermont	0
50	Virginia	0
51	Washington	0
52	Wyoming	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Lincoln	Arkansas	165,003	1	99
Chattahoochee	Georgia	156,413	2	99
Trousdale	Tennessee	151,896	3	99
Lafayette	Florida	148,302	4	99
Lake	Tennessee	135,405	5	99
Davidson	Tennessee	44,047	225	92
Richland	South Carolina	38,849	336	89
York	South Carolina	21,137	1324	57
Orange	California	18,136	1596	49
Pierce	Washington	10,817	2353	25

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Hancock	Georgia	5,203	1	99
Kenedy	Texas	4,950	2	99
Emporia city	Virginia	4,863	3	99
Galax city	Virginia	4,727	4	99
Randolph	Georgia	4,279	5	99
Richland	South Carolina	613	761	75
Davidson	Tennessee	462	1066	66
Orange	California	422	1162	63
York	South Carolina	310	1460	53
Pierce	Washington	259	1640	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.